Abstract. Analyzing network environments for security flaws and assessing new service and infrastructure configurations in general are dangerous and error-prone when done in operational networks. Therefore, cloning such networks into a dedicated test environment is beneficial for comprehensive testing and analysis without impacting the operational network. To automate this reproduction of a network environment in a physical or virtualized testbed, several key features are required: (a) a suitable network model to describe network environments, (b) an automated acquisition process to instantiate this model for the respective network environment, and (c) an automated setup process to deploy the instance to the testbed.With this work, we present INSALATA, an automated and extensible framework to reproduce physical or virtualized network environments in network testbeds. INSALATA employs a modular approach for data acquisition and deployment, resolves interdependencies in the setup process, and supports just-in-time reproduction of network environments. INSALATA is open source and available on Github. To highlight its applicability, we present a real world case study utilizing INSALATA.
Conducting experiments in federated, distributed, and heterogeneous testbeds is a challenging task for researchers. Researchers have to take care of the whole experiment life cycle, ensure the reproducibility of each run, and the comparability of the results. We present GPLMT, a flexible and lightweight framework for managing testbeds and the experiment life cycle. GPLMT provides an intuitive way to formalize experiments. The resulting experiment description is portable across varying experimentation platforms. GPLMT enables researchers to manage and control networked testbeds and resources, and conduct experiments on large-scale, heterogeneous, and distributed testbeds. We state the requirements and the design of GPLMT, describe the challenges of developing and using such a tool, and present selected user studies along with their experience of using GPLMT in varying scenarios. Keywords: testbed management·experimentationnodes in a precise timely manner to control the execution. At the end, the results need to be collected from all nodes. Monitoring and error handling also have to be considered, as resources may become unavailable, or a sub-task may fail. At worst, an experiment lasting several days has to be repeated.A large variety of testbeds is available to researchers. Many of them focus on a specific domain (e.g. wireless experimentation, high precision measurements, real-world network testbeds), and most of them use a proprietary and domainspecific approach to how the testbed is designed, accessed, managed, and experiments are controlled, requiring a manual adaptation for every experiment. When trying to transfer such an experiment to a different testbed, the experimenter has to adapt-and most of the time rewrite-the experiment to be able to transfer the experiment to a different platform. This makes it difficult to reproduce and confirm experiment results for both the researcher as well as the research community.These tasks are similar to many experiments but are still performed by most experimenters manually, or with the help of ad-hoc scripts which are rarely reusable. Instead of implementing ad hoc solutions specific to our particular problems, we decided to realize a flexible and extensible testbed and experimentation tool, supporting us in our work and to make it available to the public.With this work, we present GPLMT, a flexible, lightweight experimentation and testbed management tool. GPLMT provides an intuitive way for users to define experiments, supports the full experimentation life cycle, and allows experiments to be transferred between different testbeds and platforms, ensuring reproducibility and comparability of experiment results. GPLMT is free software and its source code is publicly available on the GPLMT website 1 . In the remainder of this paper we will give an overview of GPLMT, state the requirements and challenges for such a tool, and describe the design and implementation. In Section 7, we describe the experiences of users working with GPLMT in various scenarios.
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